Data privacy mechanisms development and performance evaluation for personal and ubiquitous blockchain-based storage

• Published in: The Journal of supercomputing 2023-11, Vol.79 (17), p.19636-19670
• Main Authors: Wen, Yean-Fu, Wang, Chia-Pei
• Format: Article
• Language: English
• Subjects: Blockchain; Compilers; Computer Science; Cryptography; Data storage; Decentralization; Interpreters; Performance evaluation; Privacy; Processor Architectures; Programming Languages; Segmentation; Storage systems
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-023-05425-7

In the era of global communication and the sharing economy, the demand for personal and ubiquitous storage among enterprises and individual users has increased; thus, the flexible expansion of the data access architecture has become crucial. According to the storage requirements, the aim of this study was to develop store and access mechanisms for achieving data privacy, decentralisation, and load balancing in a data storage system. We explore how factors such as data privacy, file segmentation, security processes, and auditing mechanisms affect the performance of ubiquitous storage systems. We developed a trust evaluation model to reduce the access error rates, which are caused by the abnormal storage hosts or transmission failed. The file segmentation and encryption methods are used to store data in untrustworthy decentralised hosts. Although such data can be decrypted, they are difficult to recognise because a part of the transformed file is adopted to protect user privacy. Furthermore, with the developed mechanism, any user can share their storage and computing power to achieve blockchain-based peer-to-peer storage functions. We conducted a series of simulations to evaluate and compare the performance of the proposed data privacy mechanism and existing data privacy mechanisms in ubiquitous storage. The results indicated that the proposed mechanism outperformed other mechanisms.